{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c544a385",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "# -----------------------------------------------------------------------------\n",
    "# Author: Zexiong Wu, Xueyou Li (wuzx58@mail2.sysu.edu.cn, lixueyou@mail.sysu.edu.cn)\n",
    "# Date: 2024-06-09\n",
    "# Description: Train PINN m-method for pile\n",
    "# Article: https://link.cnki.net/urlid/32.1124.TU.20250926.1432.002\n",
    "# -----------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0d8d28db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom neural network\n",
    "class CustomNetwork(nn.Module):\n",
    "    def __init__(self, layers, activation=nn.Tanh(), is_TanhShrink=False):\n",
    "        super().__init__()\n",
    "        activation_list = [activation] * (len(layers) - 2)\n",
    "        if is_TanhShrink:\n",
    "            activation_list[0] = nn.Tanhshrink()\n",
    "            activation_list[-1] = nn.Tanhshrink()\n",
    "        self.networks = self.create_network(layers, activation_list)\n",
    "\n",
    "    # Build the sequential network\n",
    "    def create_network(self, layers, activation_list):\n",
    "        network = nn.Sequential()\n",
    "        for i in range(len(layers)-2):\n",
    "            network.add_module('linear{}'.format(i+1), nn.Linear(layers[i], layers[i+1]))\n",
    "            network.add_module('activation{}'.format(i+1), activation_list[i])\n",
    "        network.add_module('output', nn.Linear(layers[-2], layers[-1]))\n",
    "        return network\n",
    "    \n",
    "    # Forward pass with hard constraint\n",
    "    def forward(self, inputs_list):\n",
    "        xN, LpN, MtN, VtN = inputs_list\n",
    "        inputs_tensor = torch.cat(inputs_list, dim=1)\n",
    "        outputs_tensor = self.networks(inputs_tensor)\n",
    "        # Apply hard constraint\n",
    "        y = outputs_tensor[:, 0].unsqueeze(1) * MtN + outputs_tensor[:, 1].unsqueeze(1) * VtN\n",
    "        return y\n",
    "    \n",
    "    # Output all required physical quantities\n",
    "    def outputs(self, input_list):\n",
    "        '''input_list: [x, Mt, Vt, EI, Lp, m, b0]'''\n",
    "        x, Mt, Vt, EI, Lp, m, b0 = input_list\n",
    "        alpha = (m * b0 / EI)**(1 / 5)\n",
    "        xN = x * alpha\n",
    "        LpN = Lp * alpha\n",
    "        VtN = Vt / (EI * alpha**3)\n",
    "        MtN = Mt / (EI * alpha**2)\n",
    "        y = self.forward([xN, LpN, MtN, VtN])\n",
    "        dy = gradients(y, x, 1)\n",
    "        d2y = gradients(dy, x, 1)\n",
    "        d3y = gradients(d2y, x, 1)\n",
    "        d4y = gradients(d3y, x, 1)\n",
    "        return [y, dy, d2y*EI, d3y*EI, d4y*EI]\n",
    "\n",
    "# Compute gradients of u with respect to x\n",
    "def gradients(u, x, order=1):\n",
    "    if order == 1:\n",
    "        return torch.autograd.grad(u, x, grad_outputs=torch.ones_like(u),\n",
    "                                   create_graph=True,\n",
    "                                   retain_graph=True,\n",
    "                                   only_inputs=True,)[0]\n",
    "    else:\n",
    "        return gradients(gradients(u, x), x, order=order - 1)\n",
    "\n",
    "# Mean squared error\n",
    "def MS(equ):\n",
    "    return torch.mean(equ**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82ef3c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the ranges for the parameters\n",
    "LpN_range = [1.5, 5]\n",
    "VtN_range = [0, 0.01]\n",
    "MtN_range = [0, 0.01]\n",
    "\n",
    "layers = [4] + [50] * 4 + [2]\n",
    "model = CustomNetwork(layers, is_TanhShrink=True)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)\n",
    "\n",
    "losses_list = []\n",
    "epoch_sum = 0\n",
    "t_sum = 0"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "id": "5ee2474a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch: 20000\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
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      "Epoch: 22500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [2.072031612954106e-08, 1.1515464137801246e-08, 1.0952721396506604e-08, 1.2916451019862052e-08, 4.967711131342867e-09]\n",
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      "Epoch: 23000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [2.2232912399999805e-08, 1.4419569538404176e-08, 1.578889907705161e-08, 1.565206630971261e-08, 5.666334512710591e-09]\n",
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      "loss_n : 0.0010335653787478805\n",
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      "loss:  [1.5508460293744974e-08, 7.844057847705699e-09, 8.689609032330736e-09, 8.568057374702676e-09, 3.1661722132980685e-09]\n",
      "loss_n : 0.0006453532259911299\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.6798537672002567e-08, 5.983607831439031e-09, 6.693033682836358e-09, 1.0760046187385797e-08, 4.888141447167982e-09]\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.5673755626721686e-08, 8.380375504657422e-09, 7.635945209472084e-09, 1.101510704870634e-08, 4.802582775909059e-09]\n",
      "loss_n : 0.0007003619102761149\n",
      "Epoch: 27000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.4420275640247837e-08, 8.483969082817566e-09, 7.887640762760384e-09, 9.533864364641431e-09, 3.541724913347366e-09]\n",
      "loss_n : 0.00064669648418203\n",
      "Epoch: 27500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.3075140081753034e-08, 6.402533614391359e-09, 6.195814084009044e-09, 8.285288899401166e-09, 4.049817814433254e-09]\n",
      "loss_n : 0.000560324580874294\n",
      "Epoch: 28000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.2403549298767302e-08, 5.735977914866908e-09, 6.323348511472204e-09, 8.421911168454699e-09, 3.0403808359835693e-09]\n",
      "loss_n : 0.000529610610101372\n",
      "Epoch: 28500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.3680379495895068e-08, 6.258264129144209e-09, 7.682690039700901e-09, 7.75389530360826e-09, 3.2277078787501523e-09]\n",
      "loss_n : 0.0005690864054486156\n",
      "Epoch: 29000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.690453288460958e-08, 5.016086213061044e-09, 6.289172738149773e-09, 8.58510418311198e-09, 4.211388571206953e-09]\n",
      "loss_n : 0.0006045167101547122\n",
      "Epoch: 29500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.2646907521229878e-08, 8.302313503349978e-09, 6.920457540360303e-09, 7.3005481660004534e-09, 2.6934527941335773e-09]\n",
      "loss_n : 0.000558188243303448\n",
      "Epoch: 30000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0467306132966314e-08, 6.7288938865317505e-09, 7.180320782396166e-09, 6.5020264727877475e-09, 2.5144510917840535e-09]\n",
      "loss_n : 0.0004922812804579735\n",
      "Epoch: 30500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.228635770900155e-08, 6.243284556006756e-09, 6.226460236291587e-09, 7.912991151215465e-09, 2.5871083053630173e-09]\n",
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      "Epoch: 31000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.1953605216774577e-08, 5.451730622496598e-09, 6.544789599161049e-09, 8.332277090516982e-09, 2.8276918584424493e-09]\n",
      "loss_n : 0.000517594744451344\n",
      "Epoch: 31500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.1201626293200206e-08, 6.1899601000448e-09, 5.173238726285945e-09, 8.13167755353561e-09, 3.199550624444214e-09]\n",
      "loss_n : 0.0004996973439119756\n",
      "Epoch: 32000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.7343230851452063e-08, 1.0584925824730362e-08, 9.168983794438645e-09, 1.3865671277812908e-08, 4.47988757201756e-09]\n",
      "loss_n : 0.0008173390524461865\n",
      "Epoch: 32500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.3481084693012235e-08, 7.8429893690668e-09, 6.522849815837617e-09, 8.532463624533193e-09, 3.140227633480208e-09]\n",
      "loss_n : 0.0005826001288369298\n",
      "Epoch: 33000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.9769297310290312e-08, 3.1717781734386108e-09, 2.84871859435043e-09, 9.72069802429587e-09, 4.172121759182801e-09]\n",
      "loss_n : 0.0005850030574947596\n",
      "Epoch: 33500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.077105515889798e-08, 7.69492469743227e-09, 6.665523244464566e-09, 1.0582567711026059e-08, 3.9926981720839194e-09]\n",
      "loss_n : 0.0005853591719642282\n",
      "Epoch: 34000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [9.850588789106496e-09, 4.820865928678586e-09, 5.279099379862373e-09, 5.7253659591083306e-09, 2.3872841481420437e-09]\n",
      "loss_n : 0.0004137091455049813\n",
      "Epoch: 34500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [9.733668981937171e-09, 3.6951430804066376e-09, 4.070658476962308e-09, 5.13377118593894e-09, 1.9556400943088192e-09]\n",
      "loss_n : 0.0003624905366450548\n",
      "Epoch: 35000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0122804816603548e-08, 5.26891996699419e-09, 6.5407879112910905e-09, 5.790862012133857e-09, 2.825128131433985e-09]\n",
      "loss_n : 0.00045034755021333694\n",
      "Epoch: 35500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.097308111042139e-08, 6.73499744863193e-09, 5.655235391088809e-09, 6.130046248387089e-09, 2.197291015804126e-09]\n",
      "loss_n : 0.0004671851929742843\n",
      "Epoch: 36000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [8.193465461658889e-09, 3.701029704927805e-09, 4.457559654724719e-09, 5.3804609656538105e-09, 1.9211403579078024e-09]\n",
      "loss_n : 0.0003487033536657691\n",
      "Epoch: 36500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [9.793692079540506e-09, 3.283906258033653e-09, 2.2693713574994945e-09, 5.792219592848369e-09, 2.7695734594601618e-09]\n",
      "loss_n : 0.0003524641797412187\n",
      "Epoch: 37000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [7.106941257717381e-09, 3.1847986470268097e-09, 3.7895575566437856e-09, 3.769053513735798e-09, 1.6987931061862582e-09]\n",
      "loss_n : 0.00028819445287808776\n",
      "Epoch: 37500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0385351245645325e-08, 4.52289272701023e-09, 4.647743967467477e-09, 6.38887609483163e-09, 2.478584670839723e-09]\n",
      "loss_n : 0.0004190198960714042\n",
      "Epoch: 38000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [8.071815216226241e-09, 4.82292783487992e-09, 4.3021293194556165e-09, 4.18429690896005e-09, 1.894488121934046e-09]\n",
      "loss_n : 0.0003431309014558792\n",
      "Epoch: 38500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [6.1289631148042645e-09, 3.3393117160329666e-09, 3.6795966273928116e-09, 3.5832279365877184e-09, 1.3979626345417273e-09]\n",
      "loss_n : 0.000267259543761611\n",
      "Epoch: 39000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [8.265726769707271e-09, 4.094293792888948e-09, 4.080863647004662e-09, 6.870172875039771e-09, 2.7476481090360494e-09]\n",
      "loss_n : 0.00038415874587371945\n",
      "Epoch: 39500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [7.199607132690744e-09, 2.9122007028092867e-09, 2.8700635201772684e-09, 2.8236268878600868e-09, 1.671389582291738e-09]\n",
      "loss_n : 0.0002576451515778899\n",
      "Epoch: 40000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0174930231698909e-08, 6.086540160765708e-09, 5.366484590041409e-09, 5.524330326522886e-09, 2.4887207850099458e-09]\n",
      "loss_n : 0.0004369692178443074\n",
      "Epoch: 40500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [7.925183176382689e-09, 3.479136090334123e-09, 3.0788074312226854e-09, 3.2639297931069677e-09, 9.468462680572998e-10]\n",
      "loss_n : 0.00027558644069358706\n",
      "Epoch: 41000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [6.555386011797282e-09, 5.004336500746831e-09, 5.183876439218693e-09, 4.219939953031826e-09, 1.5115705354062925e-09]\n",
      "loss_n : 0.00033132918179035187\n",
      "Epoch: 41500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [7.772134935635222e-09, 3.812492543886492e-09, 2.7351758635774104e-09, 3.82114828667568e-09, 1.2855346787077337e-09]\n",
      "loss_n : 0.0002863862318918109\n",
      "Epoch: 42000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [5.975809180824854e-09, 2.430665224650852e-09, 2.4063802062102013e-09, 2.774024565610489e-09, 1.2890216671834764e-09]\n",
      "loss_n : 0.00021930126240476966\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
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      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
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      "loss_n : 4.955676195095293e-05\n",
      "Epoch: 135000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0392378069212782e-09, 4.349223203803376e-10, 3.111026991575727e-10, 4.221738791887475e-10, 2.7246693790061727e-10]\n",
      "loss_n : 3.655886393971741e-05\n",
      "Epoch: 135500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.6326280327660925e-09, 6.144136310837212e-10, 6.369160199248824e-10, 4.872013903423067e-10, 2.2668154853189293e-10]\n",
      "loss_n : 5.303954822011292e-05\n",
      "Epoch: 136000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.8359955333835387e-08, 7.124715817319327e-10, 7.457545136979604e-10, 8.20611634200219e-10, 2.7029178895077166e-10]\n",
      "loss_n : 0.0003082428011111915\n",
      "Epoch: 136500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.3575469637316928e-09, 5.012531500980799e-10, 4.6418585641916366e-10, 7.423925918459418e-10, 3.6538419512233133e-10]\n",
      "loss_n : 5.057647649664432e-05\n",
      "Epoch: 137000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0567355879231854e-09, 4.3544237660064766e-10, 3.8635170063194835e-10, 3.552459992839374e-10, 1.7364504278916115e-10]\n",
      "loss_n : 3.5490316804498434e-05\n",
      "Epoch: 137500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.5428852639942647e-09, 8.850334354271183e-10, 8.058697598123388e-10, 5.984762130317733e-10, 2.4777382923169e-10]\n",
      "loss_n : 6.014812970533967e-05\n",
      "Epoch: 138000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.968142093744518e-09, 5.958782356429992e-10, 7.260314016654945e-10, 4.658907704069293e-10, 2.829369238899204e-10]\n",
      "loss_n : 5.9541362134041265e-05\n",
      "Epoch: 138500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0761964652772349e-09, 3.0716393317753443e-10, 3.313921359548999e-10, 3.133992787507367e-10, 1.4104370449352643e-10]\n",
      "loss_n : 3.1978386687114835e-05\n",
      "Epoch: 139000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [8.705574927603266e-09, 7.135709800820678e-10, 8.881452795428402e-10, 6.02407512761971e-10, 1.6464873908716982e-10]\n",
      "loss_n : 0.000163258591783233\n",
      "Epoch: 139500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0394428651139265e-09, 4.047594759359896e-10, 4.3705863927989697e-10, 3.068488241275702e-10, 1.504948665687067e-10]\n",
      "loss_n : 3.447582639637403e-05\n",
      "Epoch: 140000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.068719224228687e-09, 4.023776034589588e-10, 2.77035561158101e-10, 3.8284411751909886e-10, 2.310081431700084e-10]\n",
      "loss_n : 3.4820495784515515e-05\n",
      "Epoch: 140500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.2019538697671805e-09, 3.7670278008050673e-10, 2.9416410973759355e-10, 3.4975813911763964e-10, 2.3033019935780885e-10]\n",
      "loss_n : 3.616090543800965e-05\n",
      "Epoch: 141000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.2899511458996926e-09, 3.7016403831025e-10, 3.8798020352004414e-10, 4.3525655302190103e-10, 2.3794921588660145e-10]\n",
      "loss_n : 4.011755663668737e-05\n",
      "Epoch: 141500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [9.87690818021747e-10, 5.725602769679483e-10, 5.365387578670777e-10, 4.82377859878369e-10, 2.350728778299782e-10]\n",
      "loss_n : 4.1487670387141407e-05\n",
      "Epoch: 142000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.2922867220765966e-09, 5.150941895237793e-10, 4.722888191643904e-10, 3.5432273781665913e-10, 1.8240703392180535e-10]\n",
      "loss_n : 4.151950270170346e-05\n",
      "Epoch: 142500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.1169932756516232e-09, 3.48206602440726e-10, 4.12240658276275e-10, 3.256624969694144e-10, 1.604152921608204e-10]\n",
      "loss_n : 3.484310582280159e-05\n",
      "Epoch: 143000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.0347137591182332e-09, 5.206021724823984e-10, 5.311299733357089e-10, 4.0330810913147275e-10, 1.751619543854943e-10]\n",
      "loss_n : 3.9286325772991404e-05\n",
      "Epoch: 143500\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.264306326298481e-09, 3.69857866555634e-10, 3.980950014081941e-10, 3.5418620814020585e-10, 1.7127209372969077e-10]\n",
      "loss_n : 3.7705998693127185e-05\n",
      "Epoch: 144000\n",
      "['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
      "loss:  [1.1265973709484456e-09, 4.417454735339277e-10, 5.452095996894002e-10, 6.225366333545423e-10, 2.7384960965548544e-10]\n",
      "loss_n : 4.4372663978720084e-05\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 25\u001b[0m\n\u001b[0;32m     22\u001b[0m y1 \u001b[38;5;241m=\u001b[39m model([xN1, LpN, MtN, VtN])\n\u001b[0;32m     24\u001b[0m d4y \u001b[38;5;241m=\u001b[39m gradients(y, xN, \u001b[38;5;241m4\u001b[39m)\n\u001b[1;32m---> 25\u001b[0m d2y0 \u001b[38;5;241m=\u001b[39m \u001b[43mgradients\u001b[49m\u001b[43m(\u001b[49m\u001b[43my0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxN0\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     26\u001b[0m d3y0 \u001b[38;5;241m=\u001b[39m gradients(d2y0, xN0, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     27\u001b[0m d2y1 \u001b[38;5;241m=\u001b[39m gradients(y1, xN1, \u001b[38;5;241m2\u001b[39m)\n",
      "Cell \u001b[1;32mIn[6], line 53\u001b[0m, in \u001b[0;36mgradients\u001b[1;34m(u, x, order)\u001b[0m\n\u001b[0;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mgrad(u, x, grad_outputs\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mones_like(u),\n\u001b[0;32m     49\u001b[0m                                create_graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     50\u001b[0m                                retain_graph\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m     51\u001b[0m                                only_inputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m---> 53\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgradients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgradients\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[6], line 48\u001b[0m, in \u001b[0;36mgradients\u001b[1;34m(u, x, order)\u001b[0m\n\u001b[0;32m     46\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgradients\u001b[39m(u, x, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m     47\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m order \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m---> 48\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgrad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mones_like\u001b[49m\u001b[43m(\u001b[49m\u001b[43mu\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     49\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     50\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m     51\u001b[0m \u001b[43m                                   \u001b[49m\u001b[43monly_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m     52\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m gradients(gradients(u, x), x, order\u001b[38;5;241m=\u001b[39morder \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\85475\\anaconda3\\envs\\py38\\lib\\site-packages\\torch\\autograd\\__init__.py:411\u001b[0m, in \u001b[0;36mgrad\u001b[1;34m(outputs, inputs, grad_outputs, retain_graph, create_graph, only_inputs, allow_unused, is_grads_batched, materialize_grads)\u001b[0m\n\u001b[0;32m    407\u001b[0m     result \u001b[38;5;241m=\u001b[39m _vmap_internals\u001b[38;5;241m.\u001b[39m_vmap(vjp, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, allow_none_pass_through\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)(\n\u001b[0;32m    408\u001b[0m         grad_outputs_\n\u001b[0;32m    409\u001b[0m     )\n\u001b[0;32m    410\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 411\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[0;32m    412\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    413\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgrad_outputs_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    414\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    415\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    416\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[43m        \u001b[49m\u001b[43mallow_unused\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    418\u001b[0m \u001b[43m        \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    419\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m materialize_grads:\n\u001b[0;32m    421\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28many\u001b[39m(\n\u001b[0;32m    422\u001b[0m         result[i] \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_tensor_like(inputs[i])\n\u001b[0;32m    423\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(inputs))\n\u001b[0;32m    424\u001b[0m     ):\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "epochs = 500000\n",
    "Nr = 500\n",
    "MSE = torch.nn.MSELoss()\n",
    "device = 'cuda'\n",
    "model.to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=5e-5)\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    optimizer.zero_grad()\n",
    "    \n",
    "    # Input sampling\n",
    "    if epoch_sum % 100 == 0 or epoch == 0:\n",
    "        LpN = torch.rand(Nr, 1, device=device) * (LpN_range[1] - LpN_range[0]) + LpN_range[0]\n",
    "        VtN = torch.rand(Nr, 1, device=device) * (VtN_range[1] - VtN_range[0]) + VtN_range[0]\n",
    "        MtN = torch.rand(Nr, 1, device=device) * (MtN_range[1] - MtN_range[0]) + MtN_range[0]\n",
    "        xN = (torch.rand(Nr, 1, device=device) * LpN).requires_grad_(True)\n",
    "        xN0 = torch.zeros(Nr, 1, device=device, requires_grad=True)\n",
    "        xN1 = (torch.ones(Nr, 1, device=device) * LpN).requires_grad_(True)\n",
    "    \n",
    "    y = model([xN, LpN, MtN, VtN])\n",
    "    y0 = model([xN0, LpN, MtN, VtN])\n",
    "    y1 = model([xN1, LpN, MtN, VtN])\n",
    "    \n",
    "    d4y = gradients(y, xN, 4)\n",
    "    d2y0 = gradients(y0, xN0, 2)\n",
    "    d3y0 = gradients(d2y0, xN0, 1)\n",
    "    d2y1 = gradients(y1, xN1, 2)\n",
    "    d3y1 = gradients(d2y1, xN1, 1)\n",
    "    \n",
    "    # Loss functions\n",
    "    loss_gov = MS(d4y + xN * y)\n",
    "    loss_Mt = MS(d2y0 - MtN)\n",
    "    loss_Vt = MS(d3y0 - VtN)\n",
    "    loss_Mb = MS(d2y1)\n",
    "    loss_Vb = MS(d3y1)\n",
    "\n",
    "    loss_list = [loss_gov, loss_Mt, loss_Vt, loss_Mb, loss_Vb]\n",
    "    loss_legend = ['gov', 'Mt', 'Vt', 'Mb', 'Vb']\n",
    "    loss_sum = torch.sum(torch.stack(loss_list))\n",
    "    loss_sum.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    losses_list.append([loss.item() for loss in loss_list])\n",
    "    epoch_sum += 1\n",
    "    loss_n = loss_sum / torch.sum(torch.tensor(losses_list[0]))\n",
    "\n",
    "    if epoch_sum % 500 == 0:\n",
    "        print('Epoch: {}'.format(epoch_sum))\n",
    "        print(loss_legend)\n",
    "        print('loss: ', [loss.item() for loss in loss_list])\n",
    "        print('loss_n :', loss_n.item())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4b008255",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save model\n",
    "def save_model(filename, model):\n",
    "    torch.save(model.state_dict(), filename + '.pth')\n",
    "save_model('PINN_m_method_pile', model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e6bb2c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1e80cf0fd60>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plt the losses\n",
    "losses_array = np.array(losses_list)\n",
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(losses_array.shape[1]):\n",
    "    plt.semilogy(losses_array[:, i], label=loss_legend[i])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f1264825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict\n",
    "Lp = 15\n",
    "E = 2.8e7\n",
    "Vt = 60\n",
    "Mt = 700\n",
    "D = 1.5\n",
    "b0 = 2.25\n",
    "m = 9400\n",
    "\n",
    "EI = E * (np.pi * D**4) / 64\n",
    "model.to('cpu')\n",
    "x = torch.linspace(0, Lp, 100).view(-1, 1).requires_grad_(True)\n",
    "input_list = [x, Mt*torch.ones_like(x), Vt*torch.ones_like(x), EI*torch.ones_like(x),\n",
    "              Lp*torch.ones_like(x), m*torch.ones_like(x), b0*torch.ones_like(x)]\n",
    "output_list = model.outputs(input_list)\n",
    "\n",
    "plt.figure(figsize=(15, 3))\n",
    "for i in range(5):\n",
    "    plt.subplot(1, 5, i+1)\n",
    "    plt.plot(output_list[i].detach().numpy(), x.detach().numpy())\n",
    "    plt.gca().invert_yaxis()\n",
    "    plt.ylabel('x')\n",
    "    plt.xlabel(['y', '$\\Theta$', '$M$', '$F_s$', '$p$'][i])\n",
    "    plt.tight_layout()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
